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Programming Case: a Methodology for Programmatic Web Data Extraction Journal of Technology Research Volume 7 Programming case: A methodology for programmatic web data extraction John N. Dyer Georgia Southern University ABSTRACT Web scraping is a programmatic technique for extracting data from websites using software to simulate human navigation of webpages, with the purpose of automatically extracting data from the web. While many websites provide web services allowing users to consume their services for data transfer, other websites provide no such service(s) and it is incumbent on the user to write or use existing software to acquire the data. The purpose of this paper is to provide a methodology for development of a relatively simple program using the Microsoft Excel Web Query tool and Visual Basic for Applications that will programmatically extract webpage data that are not readily transferable or available in other electronic forms. The case presents an overview of web scraping with an application to extracting historical stock price data from Yahoo’s Finance® website. The case is suitable for students that have experience in an object-oriented a programming course, and further exposes students to using Excel and VBA, along with knowledge of basic webpage structure, to harvest data from the web. It is hoped that this paper can be used as a teaching and learning tool, as well as a basic template for academicians, students and practitioners that need to consume website data when data extraction web services are not readily available. The paper can also add value to student’s programming experience in the context of programming for a purpose. Keywords: Data Extraction, Web Scraping, Web Query, Web Services Copyright statement: Authors retain the copyright to the manuscripts published in AABRI journals. Please see the AABRI Copyright Policy at http://www.aabri.com/copyright.html Programmatic web data extraction, Page 1 Journal of Technology Research Volume 7 INTRODUCTION Increasingly, many individuals and organizations have the need to extract massive amounts of data from the web. The basic technique for extracting the data is web scraping, which can be loosely defined as a computer program to extract data from a website. Wed scraping is commonly used to facilitate online price comparisons, contact scraping, online product catalog scraping, weather data monitoring, economic/demographic/statistical data extraction, and web mashups, among other uses. Web scraping is also a subset of People-Oriented programming, which empowers individuals to program web-based self-fashioned tools that ultimately suit the user’s own needs (International Journal of People-Oriented Programming (IJPOP), n.d.). Often times a company releases its application program interface (API) to the public so that software developers can design products that are powered by its service for extracting data (Roos, 2007). In many cases an API doesn’t exist and the developers must write their own. Web scraping is commonly used to either extract data from a legacy system (which has no other mechanism to transfer data), or to extract data from a website which does not provide a more convenient API (Data scraping, 2015). Since most webpages are designed for human end-users, navigation and data extraction are not necessarily easily automated. As such, web scraping is typically considered a “last resort” tool, with high programming and processing overhead. It focuses on acquiring HTML formatted data from a webpage and storing the data in a variety of formats, including a text file, a worksheet, or a database. Since websites are built using HTML or XHTML, web scraping utilizes software to simulate human exploration and extraction of data from the web, pulling the data directly out of the HTML. The program then goes through all available pages and collects data and images as a human would do manually using mouse clicks and copy-and-paste. As such, any content that can be viewed on a webpage or is contained in the source code can be scraped. It is for this reason that web scraping tools programmatically automate data extraction/acquisition from a website. The focus on this case study is on developing a methodology for programmatic web data extraction when an API or other web service is not available. Note that although there are software programs and web-browser add-ins that facilitate web scraping (paid subscription and free-ware), this paper illustrates writing one’s own program. A quick Internet search will reveal many of the available web scraping programs, as well as a highly-rated Google Chrome browser extension named Web Scraper® (Web Scraper, n.d.) and a program named Data Toolbar® (Web Data Extraction Software Made Simple, n.d.). A good overview of web scraping can be found at Brody, H. (2012), while a good instructional resource can be found from Brody, H. (2013). To visualize web scraping, consider an online product catalog wherein a certain category of products will result in many items displayed on a webpage, but the catalog will display only a subset of all the items per webpage (usually displayed in an HTML table element). That is, a single web page may display 20, 50 or 100 items per page, with paging links allowing navigation across all subsequent webpages. Such is the case with auction sites such as eBay®, wherein a search may result in 50 items per page across multiple webpages. Although many websites allow the user to choose a fixed number of items per page, few websites offer a single page view of unlimited size. Now consider the task of downloading the data for each item across each webpage. If a single page view of all the items were available the task would simply be that of a few keystrokes allowing one to select the desired data, copy the data, and then paste the data into Programmatic web data extraction, Page 2 Journal of Technology Research Volume 7 document file or worksheet. But, it is easy to imagine a website wherein thousands of items are displayed across many webpages, hence one is limited to a small subset in each webpage view. Without some type of automation, it would require one to navigate through each webpage and linked page, selecting the desired data, copying, and then pasting each of the webpage’s contents. This is obviously not a practical approach, especially if one wanted to acquire the complete contents of a large website on a frequent basis. Fortunately, if a website is structured appropriately, Excel contains all the tools necessary to automate the process of paging through and acquiring all the desired data; relatively quickly and efficiently. An advantage of using Excel is its relative ease of use and a high degree of familiarity among many business professionals and programmers, and relative familiarity among students. Excel is an excellent tool for analyzing data, including charts, sorting, filtering, and data modeling, among many used. Additionally, Excel has VBA programming capabilities that allow one to use a subset of Visual Basic to automate and manipulate Excel and other Microsoft applications, including access to the COM and ActiveX Objects, as well as a multitude of built- in functions. This case describes the methodology for programmatically extracting hundreds and thousands of historical stock price data over hundreds of webpages from Yahoo’s Finance website using the Excel Web Query tool. The stock price data example is being used since the website structure accommodates use of Excel in the manner described, and the task is very similar to how one would go about extracting data from numerous other structurally similar websites. Additionally, Yahoo has no express policies preventing programmatic data extraction. As such, Section 2 describes the manual technique for extracting a single page of records, while Section 6 describes the fully automated programmatic approach returning thousands of records across hundreds of webpages. Section 3 discusses webpage structure which is essential in the automation process, and Section 4 discusses discovery in the context of Yahoo’s Finance webpage structure. Section 5 provides the necessary overview of Excel and VBA required to automate the data extraction. One should note that the Yahoo Finance historical prices webpage has a downloadable csv file containing all the data over the specified period of data. Nevertheless, this case is to illustrate a program when no such file or other data extraction technology is readily available. EXTRACTING DATA USING THE EXCEL WEB QUERY TOOL The Excel Web Query (WQ) tool facilitates bringing data from a website into an Excel worksheet. Web queries are an easy, built-in way to bring data into Excel from the Web. The WQ tool allows the user to point a web query at an HTML document that resides on a Web server and pull part or all of the contents into your spreadsheet. The WQ tools can also retrieve refreshable data that is stored on the Internet, such as a single table, multiple tables, or all of the text on a webpage (Import external data from a complex web site into Excel, n.d.). The tool is based on discovering HTML tables on the webpage and allowing the user to select the table(s) containing the data that is desired to download. In its simplest deployment, one initiates a new web query in Excel and enters a uniform resource locator (url) into the WQ address field. The WQ navigates to the webpage and displays an icon image beside all HTML tables in the webpage. The user can select one or more tables by clicking the icon images. The text from all selected tables is downloaded and displayed in the Excel worksheet. A more extensive description of the WQ tool is available by Rice, F. (2004). As a quick example we will use a web Programmatic web data extraction, Page 3 Journal of Technology Research Volume 7 query to download one page of historical stock price data for Wal-Mart® from Yahoo Finance.
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